Hotaka Takizawa

A detection method of ground glass opacities in chest x-ray CT images using automatic clustering techniques

In this paper, we described an algorithm of automatic detection of Ground Glass Opacities (GGO) from X-ray CT images. In this algorithm, first, suspicious shadows are extracted by our Variable N-Quoit (VNQ) filter which is a type of Mathematical Morphology filters. This filter can detect abnormal shadows with high sensitivity. Next, the suspicious shadows are classified into a certain number of classes using feature values calculated from the suspicious shadows. In our traditional clustering method, a medical doctor has to manually classify the suspicious shadows into 5 clusters. The manual classification is very hard for the doctor. Thus, in this paper, we propose a new automatic clustering method which is based on a Principal Component (PC) theory. In this method, first, the detected shadows are classified into two sub-clusters according to their sizes. And then, each sub-cluster is further classified into two sub-sub-clusters according to PC Scores(PCS) calcuated from the feature values of the shadows in the sub-cluster. In this PCS-based classification, we use a threshold which maximizes the distance between the two sub-sub-clusters. The PCS-based classification is iterated recursively. Using discriminate functions based on Mahalanobis distance, the suspicious shadows are determined to be normal or abnormal. This method was examined by many samples (including GGOs shadows) of chest CT images, and proved to be very effective.

Recognition of lung nodules from x-ray CT images using 3D Markov random field models

In this paper we propose a new recognition method of lung nodules from x-ray CT images using 3D Markov random field (MRF) models. Pathological shadow candidates are detected by our Quoit filter which is a kind of mathematical morphology filter, and volume of interest (VOI) areas which include the shadow candidates are extracted. The probabilities of the hypotheses that the VOI areas come from nodules (which are candidates of cancers) and blood vessels are calculated using nodule and blood vessel models evaluating the relations between these object models using 3D MRF models. If the probabilities for the nodule models are higher, the shadow candidates are determined to be abnormal. Otherwise, they are determined to be normal. Experimental results for 38 samples (patients) are shown.

Recognition of lung nodules from X-ray CT images using 3D Markov random field models

In this-paper we propose a new recognition method of lung nodule from X-ray CT images using 3D Markov random field (MRF) models. Pathological shadow candidates are detected by a mathematical morphology filter and volume of interest (VOI) areas which include the shadow candidates are extracted. The probabilities of the hypotheses that the VOI areas come from nodules (which are candidates of cancers) and blood vessels are calculated using nodule and blood vessel models evaluating the relations between these object models by 3D MRF models. If the probabilities for the nodule models are higher, the shadow candidates are determined to be abnormal. By applying this new recognition method to actual 38 CT images, good results were obtained.

Recognition method of lung nodules using blood vessel extraction techniques and 3D object models

In this paper, we propose a method for reducing false positives in X-ray CT images using ridge shadow extraction techniques and 3D geometric object models. Suspicious shadows are detected by our variable N-quoit (VNQ) filter, which is a type of mathematical morphology filter. This filter can detect lung cancer shadows with the sensitivity over 95[%], but it also detects many false positives which are mainly related to blood vessel shadows. We have developed two algorithms to distinguish lung nodule shadows from blood vessel shadows. In the first algorithm, the ridge shadows, which come from blood vessels, are emphasized by our Tophat by Partial Reconstruction filter which is also a type of mathematical morphology filter. And then, the region of the ridge shadow is extracted using binary distance transformation. In the second algorithm, we propose a recognition method of nodules using 3D geometric lung nodule and blood vessel models. The anatomical knowledge about the 3D structures of nodules and blood vessels can be reflected in recognition process. By applying our new method to actual CT images (37 patient images), a good result has been acquired.

A Recognition Method of Lung Nodule Shadows in X-Ray CT Images Using 3D Object Models

In this paper, we describe a recognition method of lung nodule shadows in X-ray CT images using 3-dimensional nodule and blood vessel models. From these 3D object models, artificial CT images are generated as templates. The templates are then applied to input images which comprise of suspicious shadows. If any parameters of the suspicious shadow matches a nodule template rather than any blood vessel template, then it is determined to be abnormal. Otherwise, it is determined to be normal. By applying our new method to the actual lung CT images of 38 patients, the false positive ratio is reduced to 4.31 [shadow/patient] with the sensitivity exceeding 95%.

Automatic detection method of lung cancers including ground-glass opacities from chest x-ray CT images

In this paper, we described an algorithm of automatic detection of ground glass opacities (GGO) from X-ray CT images. In this algorithm, at first, pathological shadow candidates are extracted by our variable N-Quoit filter which is a kind of mathematical morphology filter. Next, shadow candidates are classified into some classes using feature values calculated from the shadow candidates. By using discriminate functions, at last, shadow candidates are discriminated between normal shadows and abnormal ones. This method was examined by 38 samples (including GGOs shadows) of chest CT images, and proved to be very effective.

A CAD system for lung cancer screening test by X-ray CT

Abstract In this paper, we report our computer-aided detection (CAD) system of lung cancer screening by CT (LSCT). LSCT is a newly developed mobile-type CT scanner for the mass screening of lung cancer by our project team. In this new LSCT system, one essential problem is the increase of image information to be diagnosed by a doctor to about 30 slices per patient. To solve this difficult problem, we have been developing a CAD system to support the doctors diagnosis activity.

Eigen nodule: view-based recognition of lung nodule in chest X-ray CT images using subspace method

We previously proposed a recognition method of lung nodules based on the experimentally selected feature values (such as contrast, circularities, etc.) of pathological candidate regions detected by our Quoit filter. In this paper, we propose a new recognition method of lung nodule using each CT value itself in ROI (region of interest) area as a feature value. In the clustering stage, the pathological candidate regions are first classified into some clusters using the principal component (PC) theories. A set of CT values in each ROI is regarded as a feature vector, and then eigen vectors and eigen values are calculated for each cluster by applying the principal component analysis (PCA). The eigen vectors (we call them eigen images) corresponding to the 10 largest eigen values, are utilized as base vectors for subspaces of the clusters in the feature space. In the discrimination stage, correlations are measured between the testing feature vector and the subspace which is spanned by the eigen images. If the correlation with the abnormal subspace is large, the pathological candidate region is determined to be abnormal. Otherwise, it is determined to be normal. By applying our new method, good results have been acquired.

Recognition of lung nodules from X-ray CT images using 3D MRF models

Abstract In this paper, we propose a new recognition method of lung nodules from X-ray CT images using 3D Markov random field (MRF) models with geometrical object models. Some results acquired by this method to actual CT images are shown.

An efficient recognition method of lung nodules from X-ray CT images using 3-D object models

In this paper, we propose an efficient algorithm to detect candidates of nodule shadows from X-ray CT images using 3D geometric object models of cancers and blood vessels. By using such 3D geometric object models, the anatomical knowledge about the 3D structures of nodules and blood vessels can be reflected in recognition process. Additionally, we improve the performance of the recognition method using template matching techniques. The template images are generated from the object models before the recognition process, and all of the template images can be memorized in the computers. As a result of the improvement of the performance, the calculation time of the recognition method is decreased drastically. By applying our new method to actual CT images (38 patient images), a good result has been acquired.